Direct blind MMSE channel equalization based on second-orderstatistics

A family of new MMSE blind channel equalization algorithms based on second-order statistics are proposed. Instead of estimating the channel impulse response, we directly estimate the cross-correlation function needed in Wiener-Hopf filters. We develop several different schemes to estimate the cross-correlation vector, with which different Wiener filters are derived according to minimum mean square error (MMSE). Unlike many known sub-space methods, these equalization algorithms do not rely on signal and noise subspace separation and are consequently more robust to channel order estimation errors. Their implementation requires no adjustment for either single- or multiple-user systems. They can effectively equalize single-input multiple-output (SIMO) systems and can reduce the multiple-input multiple-output (MIMO) systems into a memoryless signal mixing system for source separation. The implementations of these algorithms on SIMO system are given, and simulation examples are provided to demonstrate their superior performance over some existing algorithms     

Block channel equalization in the frequency domain

In this paper, channel equalization algorithms processing two samples of the received signal per channel symbol and operating in the frequency domain are described in a unifying framework. First, minimum mean-square error linear and decision-feedback equalizers are derived, and a synthesis technique based on the well-known Levinson-Durbin algorithm is proposed for the latter. Then, iterative linear and decision-feedback equalization algorithms for turbo processing are devised. Performance results for both uncoded and coded phase-shift keying transmissions show the efficacy of the proposed equalization techniques and their superiority over other existing frequency-domain equalization strategies.     

基于Turbo均衡和信道估计的单通道盲信号恢复算法

定时同步是单通道盲信号接收端处理的难点,提出了一种无需定时同步基于Turbo均衡的单通道盲信号恢复算法。该算法将定时同步偏差等效为符号间干扰（ISI,inter-symbol interference）信道,通过信道估计和Turbo均衡相互反馈软信息来改善源信号信息恢复性能。重点研究了初始盲均衡算法、信道估计算法、混合信号的MMSE均衡算法以及三者间的软信息交互。算法复杂度低、计算量小,适用于高阶调制信号。仿真结果表明,对BPSK、QPSK和8PSK信号,该算法都能得到较好的性能,且对等功率和不等功率信号同样适用。     

低空无线信道建模及其均衡技术研究进展

在介绍航空信道建模基础上 ,借鉴地面移动信道研究成果,分析了描述低空无线信道特性的常用参数,对不同场景下低 空信道的多径时延、多普勒频移等传输特性作了详细介绍,给出了低空信道建模的一般模型 。介绍了低空无线信道常用的LMS、RLS、CMA 三种信道均衡算法。最后,对低空无线信道建模研究以及适合低空信道的均衡算法进行展望 , 指出在改进现有均衡算法、编码和均衡相结合、最大多普勒估计和自适应均衡相结合等方 面是值得关注的研究内容。     

基于回声状态网络的卫星信道在线盲均衡算法

针对非线性卫星信道,该文提出了两种基于回声状态网络(ESN)的在线盲均衡算法。利用ESN良好的非线性逼近能力,将发送信号的高阶统计量(HOS)代入ESN,结合常模算法(CMA)和多模算法(MMA)构造盲均衡的代价函数,并采用递归最小二乘(RLS)算法对ESN输出权值进行迭代寻优,实现了Volterra卫星信道下常模和多模信号的在线盲均衡。实验表明,该文算法可以有效降低非线性信道对发送信号产生的畸变,相较于传统的Volterra滤波方法,有更快的收敛速度和更低的均方误差值。     

基于盲源分离的水声信道盲均衡处理方法

提出了一种基于盲源分离的水声信道讯均衡处理方法,通过对接收信号过采样构成源信号,采用了基于信息最大化原理(Infomax)在线分离算法进行了水声信道的盲均衡,并研究了时变水声信道条件下算法的均衡情况,仿真实验结果表明,该处理方法对多径水声信道具有较好的均衡效果,同时不受最小相位的条件限制。     

一种基于时空编码的物理层安全算法

针对多天线窃听系统的物理层安全问题,提出一种基于时空编码的预编码算法。首先,合法接收者发送训练序列用于发送者估计主信道状态信息,而窃听信道的状态信息合法用户均未知;其次,发送者利用均匀信道分解的方法提取主信道状态信息的特征参数,生成发射端预编码矩阵和合法接收端均衡矩阵,收发联合加密处理提高物理层安全;最后,利用 Monte Carlo方法进行仿真实验,数值分析表明,该算法在窃听者天线数目增多时能够实现非负的保密容量,即使窃听信道质量较好时,窃听者的接收性能仍维持在很差的水平,误码率高达0.5。     

基于FFT的非线性通道均衡算法研究

在频域通道均衡中求得均衡函数后,一个关键技术是寻找恰当的均衡滤波器。本文针对传统均衡中宽带信号高频部分拟合精度较差的问题,利用FFT（快速傅里叶变换）,提出了雷达通道非线性均衡处理算法。通过雷达信号和雷达通道的仿真数据,将运用该算法和传统算法均衡处理的结果进行了直观效果和失配指标比较。实验结果表明,基于FFT的非线性通道均衡算法能有效地抑制高频失配;而且由该算法得到的幅度失配和相位失配指标均优于基于FFT的传统的线性通道均衡算法和基于最小二乘的均衡算法。     

Advanced Low Power High Speed Nonlinear Signal Processing: An Analog VLSI Example.

Despite the progress made in digital signal processing during the last decades, the constraints imposed by high data rate communications are becoming ever more stringent. Moreover mobile communications raised the importance of power consumption for sophisticated algorithms, such as channel equalization or decoding. The strong link existing between computational speed and power consumption suggests an investigation of signal processing with energy efficiency as a prominent design choice. In this work we revisit the topic of signal processing with analog circuits and its potential to increase the energy efficiency. Channel equalization is chosen as an application of nonlinear signal processing, and a vector equalizer based on a recurrent neural network structure is taken as an example to demonstrate what can be achieved with state of the art in VLSI design. We provide an analysis of the equalizer, including the analog circuit design, system-level simulations, and comparisons with the theoretical algorithm. First measurements of our analog VLSI circuit confirm the possibility to achieve an energy requirement of a few pJ/bit, which is an improvement factor of three to four orders of magnitude compared with today’s most energy efficient digital circuits.     

基于最大相关熵准则的恒模盲均衡算法

针对恒模算法(constant modulus algorithm, CMA)在脉冲噪声环境下性能退化的问题,本文基于最大相关熵准则(maximum correntropy criterion, MCC)对恒模算法中基于最小均方误差(mean square error, MSE)准则的代价函数进行修正,推导出适用于脉冲噪声环境的基于MCC准则的恒模盲均衡算法(MCC_CMA)。该算法利用通信信号的恒模特性,首先得到发送信号与均衡器输出信号模值的误差信号,再通过使模值误差信号的相关熵最大来获得其迭代误差调节项,避免了传统高阶统计量算法在脉冲噪声环境下性能退化的问题。对高斯噪声以及α-稳定分布和混合高斯分布两种脉冲噪声环境下的信道均衡问题的仿真实验表明,相对于经典的自适应恒模盲均衡算法,MCC_CMA算法不依赖噪声的先验知识就能获得较快的收敛速度、较低的剩余码间干扰和误码率,并且在不同脉冲强度的脉冲噪声环境下都能够得到较好的均衡结果,表明MCC_CMA算法具有很好的鲁棒性。      

QR factorization based blind channel identification withsecond-order statistics

Most eigenstructure-based blind channel identification and equalization algorithms with second-order statistics need SVD or EVD of the correlation matrix of the received signal. In this paper, we address new algorithms based on QR factorization of the received signal directly without calculating the correlation matrix. This renders the QR factorization-based algorithms more robust against ill-conditioned channels, i.e., those channels with almost common zeros among the subchannels. First, we present a block algorithm that performs the QR factorization of the received data matrix as a whole. Then, a recursive algorithm is developed based on the QR factorization by updating a rank-revealing ULV decomposition. Compared with existing algorithms in the same category, our algorithms are computationally more efficient. The computation in each recursion of the recursive algorithm is on the order of O(m²) if only equalization is required, where m is the dimension of the received signal vector. Our recursive algorithm preserves the fast convergence property of the subspace algorithms, thus converging faster than other adaptive algorithms such as the super-exponential algorithm with comparable computational complexities. Moreover, our proposed algorithms do not require noise variance estimation. Numerical simulations demonstrate the good performance of the proposed algorithms     

宽带阵列通道均衡的工程实现方法研究

对于宽带数字阵列雷达而言,通道之间的幅相特性不一致(即通道失配)会影响雷达阵列信号处理的性能,需要对各通道进行通道均衡来补偿通道失配的损失。基本的通道均衡算法分为时域、频域以及自适应均衡算法,文中通过仿真比较了各类型算法的均衡能力,并着重考虑了参考通道的选择对均衡结果的影响,由此得到通道均衡的最优解决方法。最后,通过雷达工程实验测试,验证了文中宽带通道均衡方法的有效性及最优结果,所得结论对于实际工程设计中改善宽带波束性能具有一定借鉴意义。     

Advanced signal-processing algorithms for energy-efficient wirelesscommunications

Substantial progress has been made in the receiver signal-processing algorithms for wireless communications to minimize the requirements on signal-to-noise (and/or interference) power ratio and computational complexities for the same quality of service. In cellular infrastructure systems, one of the key system design objectives in the base stations is to maximize the receiver sensitivity, so that the required signal level from the mobile stations can be minimized. The use of advance signal-processing algorithms, based on maximum a posteriori (MAP) estimation, iterative (turbo) channel estimation, equalization, and decoding, allows for a reduction of the required transmitter power by one-third to one-half. Lower computational complexities in the terminals, which implies a reduced power drain on the digital circuits, can be achieved by using techniques that adapt the state complexity of the receiver to the propagation channel. We give an in-depth review of these algorithms, and discuss their performance and implementation requirements     

Bounds on SIMO and MIMO Channel Estimation and Equalization with Side Information

Constrained Cramér-Rao bounds are developed for convolutive multi-input multi-output (MIMO) channel and source estimation in additive Gaussian noise. Properties of the MIMO Fisher information matrix (FIM) are studied, and we develop the maximum rank of the unconstrained FIM and provide necessary conditions for the FIM to achieve full rank. Equality constraints on channel and signal parameters provide a means to study the potential value of side information, such as training symbols (semi-blind case), constant modulus (CM) sources, or known channels. Nonredundant constraints may be combined in an arbitrary fashion, so that side information may be different for different sources. The bounds are useful for evaluating the performance of SIMO and MIMO channel estimation and equalization algorithms. We present examples using the constant modulus blind equalization algorithm. The constrained bounds are also useful for evaluating the relative value of different types of side information, and we present examples comparing semi-blind, constant modulus, and known channel constraints. While the examples presented are primarily in the communications context, the CRB framework applies generally to convolutive source separation problems.     

莱斯信道的MLSD技术研究

主要研究在时变多径衰落信道下,基于Viterbi算法的最大似然序列检测技术（MLSD）.为了解决MLSD信道估计问题,使均衡器能够及时跟踪信道的变化,提出了基于逐幸存处理（PSP）和最小存活路径（MSP）的MLSD算法.在莱斯信道环境下,对上述算法进行了仿真.仿真结果表明,将RLS-MSP算法与减状态MLSD算法相结合的信道均衡技术具有对复杂信道的强跟踪能力,同时保证了较高的均衡性能和合理的复杂度.     

信道估计中基于变换域的降噪处理技术

本文讨论了信道估计中的降噪处理技术，根据信道中加性高斯噪声的特点，提出在变换城中抑制噪声能量的方法。大大提高了估计精度。文中分析了两种降噪算法，并从理论上分析了算法一的处理增益。仿真表明．经过降噪算法二处理后信道估计精度得到了大犬的提高。其估计误差时系统误码性能的影响已经极小，单纯靠提高信道估计精度已很难进一步改善系统的误码性能。     

Online blind equalization algorithm with echo state network based on prediction principle

In view of the nonlinear channel,the online blind equalization algorithm with echo state network was proposed based on prediction principle.In the proposed algorithm,the traditional linear prediction error filter was replaced by the ESN with good nonlinear mapping ability,and recursive least square (RLS) algorithm was used to calculate the output weight of the network to minimize the network prediction error.Then,the amplitude and phase were adjusted.Simulation results show that the proposed algorithm can effectively reduce the distortion caused by nonlinear channel to the transmitted signal for 16QAM signal,which has lower mean square error and faster convergence speed in comparison with other blind equalization algorithms based on prediction principle.     

一种基于STBC的SC-FDE系统双天线联合检测算法

针对双发双收的基于空时分组编码的单载波频域均衡(STBC-SC-FDE)系统,研究了一种双天线联合检测算法,通过信道估计、导频干扰消除、加权联合均衡等过程重构发送的信息。并根据信噪比对频域信号与均衡矩阵进行加权处理,使联合检测算法适用于2路接收信号信噪比不同的情况。仿真结果表明,所提出的双天线联合检测算法在SUI-3信道下最大可获得约8dB的接收分集增益。与时域最大比合并(MRC)算法相比,由于采用联合检测,该算法能有效改善系统在多径衰落信道下的误码率。     

CMA算法盲均衡性能分析

由于受多径传输和有限带宽的影响,无线信道不可避免地存在码间干扰和信道间干扰.盲均衡技术是一种不需要训练序列就能补偿信道畸变的新兴自适应均衡技术.文中从Bussgang类盲均衡原理出发,讨论了属于Bussgang类的CMA盲均衡算法,并利用Simulink对算法进行了仿真.从仿真结果可看出步长对算法收敛性能和精度的影响,步长越小,收敛速度越慢,但稳态误差越小;反之亦然.     

一种适用于稀疏多径信道的自适应均衡算法

针对传统的自适应均衡算法在稀疏多径信道下性能表现不佳的问题,提出了一种基于基追踪降噪的自适应均衡算法。该算法利用稀疏多径信道下均衡器权值的稀疏性,将自适应均衡器的训练过程看作压缩感知理论中稀疏信号对字典的加权求和,并利用重构算法直接对稀疏权值进行求解,解决了迭代参数设置和收敛慢的问题。采用基追踪降噪作为重构算法并选用变量分离近似稀疏重构对该最优化问题进行求解,既提高了权值的重构精度又降低了计算的复杂度。仿真结果表明,所提算法能够以较低的计算量和较少的训练序列达到更优性能,这对提升系统的通信性能具有参考价值。